5 Minutes, 14 Questions, and a Trust Score We Didn't Trust

5 Minutes, 14 Questions, and a Trust Score We Didn't Trust

OkCupid is a US-based online dating app and website that allows users to find romantic partners, friendships, and networking opportunities based on shared interests and values. Users answer thousands of questions about lifestyle, beliefs, and preferences. Its algorithm calculates a Match % to indicate compatibility with other users

user research

ux design

Concept Design

Journey Mapping

mobile app

Role

Product Designer (Team Lead)

Type

Project

Timeline

12 days

Year

2025

industry

Partner Matchmaking

Overview

Overview

OkCupid sells itself on one idea that most matching apps don't touch - a match percentage built from shared values, not just proximity and a photo. That's the whole differentiator — trust you, not just show you.

OkCupid sells itself on one idea that most matching apps don't touch - a match percentage built from shared values, not just proximity and a photo. That's the whole differentiator — trust you, not just show you.

OkCupid sells itself on one idea that most matching apps don’t touch - a match percentage built from shared values, not just proximity and a photo. That’s the whole differentiator — trust you, not just show you.

We went in to explore what a first-time signup actually feels like, with no fixed hypothesis and no read-up beforehand; the only thing we knew going in was that this "trust score" existed somewhere in the app.

What we found instead was a signup flow that undermines its own pitch at almost every step: unverified photos, ignored preferences, and a paywall that interrupts before you've even finished evaluating anyone.

What we found instead was a signup flow that undermines its own pitch at almost every step: unverified photos, ignored preferences, and a paywall that interrupts before you've even finished evaluating anyone.

What we did?

What we did?

An onboarding sequence staged around intent instead of one long generic form, and — most importantly — a "Why this match?" panel that finally shows the trust score its work.

An onboarding sequence staged around intent instead of one long generic form, and — most importantly — a "Why this match?" panel that finally shows the trust score its work.

Problem

Problem

We started the signup with no priors — one field per page, the way any new user would. We noticed, to complete onboarding took over five minutes, which isn't unreasonable on its own for an app asking you to build a real profile — but a phone number request dropped mid-flow, and personality questions ("would you rather be normal or weird?") that ignored the intent we'd just selected, made that time feel spent on the wrong things rather than simply spent.

We started the signup with no priors — one field per page, the way any new user would. We noticed, to complete onboarding took over five minutes, which isn't unreasonable on its own for an app asking you to build a real profile — but a phone number request dropped mid-flow, and personality questions ("would you rather be normal or weird?") that ignored the intent we'd just selected, made that time feel spent on the wrong things rather than simply spent.

We started the signup with no priors — one field per page, the way any new user would. We noticed, to complete onboarding took over five minutes, which isn’t unreasonable on its own for an app asking you to build a real profile — but a phone number request dropped mid-flow, and personality questions (“would you rather be normal or weird?”) that ignored the intent we’d just selected, made that time feel spent on the wrong things rather than simply spent.

Discover itself opened with a "boost now" paywall before the tutorial even began. The swipe tutorial, a "turn on notifications" interrupt, a legitimate "tap for next photo" step, then a separate step disguised as tutorial that was really a push toward buying a superlike — four interruptions before we saw a real profile.

Discover itself opened with a "boost now" paywall before the tutorial even began. The swipe tutorial, a "turn on notifications" interrupt, a legitimate "tap for next photo" step, then a separate step disguised as tutorial that was really a push toward buying a superlike — four interruptions before we saw a real profile.

When we finally scrolled through some profiles, the mismatch was immediate: we'd set our age preference to 25+, and were shown a 15-year-old.

When we finally scrolled through some profiles, the mismatch was immediate: we'd set our age preference to 25+, and were shown a 15-year-old.

The moment it became undeniable came later. We'd added a profile picture and a few photos during onboarding, and after finishing it, went to change one

The moment it became undeniable came later. We'd added a profile picture and a few photos during onboarding, and after finishing it, went to change one

Profile

Profile

Settings

Settings

Edit

Edit

Tapping a photo opened it full-screen with a small edit icon at bottom-center, the obvious next move. We tapped it expecting to swap the image. Instead, it opened a caption field. That was the real trigger point: if something as basic as changing a photo didn't do what it looked like it should, nothing else in the app could be taken at face value either.

Tapping a photo opened it full-screen with a small edit icon at bottom-center, the obvious next move. We tapped it expecting to swap the image. Instead, it opened a caption field. That was the real trigger point: if something as basic as changing a photo didn't do what it looked like it should, nothing else in the app could be taken at face value either.

What we found?

What we found?

1.

1.

The preference flow ignores the intent you already gave it.

The preference flow ignores the intent you already gave it.

Completing your profile triggers 14+ questions with no skip and no save-and-exit — set "hookup," and you're still asked about settling down.

2.

2.

Paywalls interrupt before you can evaluate anything.

Paywalls interrupt before you can evaluate anything.

A small scroll on Likes triggers a full-screen buy prompt; a "dealbreaker" toggle that looks like a normal switch opens a paywall instead; Messages alone stacks three separate price points (premium, boost, superboost).

3.

3.

No photo, no visibility, no notice

No photo, no visibility, no notice

Your profile with no picture gets shadow-banned from discovery with zero warning.

(hidden the face on purpose to maintain privacy ))

4.

4.

Nothing verifies what gets uploaded.

Nothing verifies what gets uploaded.

We got to know about this by casually uploading a photo of a UNO cards as a profile picture. It was accepted. This was another cue why even we won't trust what's coming in our discovery.

We opened preferences tab to update it and a inline banner was there nudging us to "complete profile" — we tapped it with no idea how many questions were coming. Past 15 questions, with no option to skip or save midway: finish the whole thing, or drop out and lose your progress.

We opened preferences tab to update it and a inline banner was there nudging us to "complete profile" — we tapped it with no idea how many questions were coming. Past 15 questions, with no option to skip or save midway: finish the whole thing, or drop out and lose your progress.

That depth was genuinely double-edged. More questions should mean sharper matching, but our intent was set to "short-term," and we were still being asked about wanting kids and political views.

The same thoroughness that could make this app's matching stronger is exactly what made it feel invasive and mistimed here.

Taken one at a time, these read like a list of bugs. But together, they pointed to one thing: nothing in the app gives users a reason to believe what they're being shown.


That was the question we needed data to explore and find out!

Taken one at a time, these read like a list of bugs. But together, they pointed to one thing: nothing in the app gives users a reason to believe what they're being shown.


That was the question we needed data to explore and find out!

Research

Research

Our own walkthrough could easily have been a bad first impression, one account, one set of eyes. Before designing around it, we needed to know if it held up beyond us.

Our own walkthrough could easily have been a bad first impression, one account, one set of eyes. Before designing around it, we needed to know if it held up beyond us.

Primary research

Primary research

We ran a short, objective-only intent survey through JotForm, posted a Reddit post that reached 2,000+ impressions. The form pulled 80+ responses across India, the US, and UK, and 5+ volunteers willing to talk further.

We ran a short, objective-only intent survey through JotForm, posted a Reddit post that reached 2,000+ impressions. The form pulled 80+ responses across India, the US, and UK, and 5+ volunteers willing to talk further.

Secondary research

Secondary research

Cross-checked against App Store and Play Store reviews, Reddit threads, and existing reports — benchmarked against Hinge, Bumble, and Tinder — the pattern held, and got sharper.

Cross-checked against App Store and Play Store reviews, Reddit threads, and existing reports — benchmarked against Hinge, Bumble, and Tinder — the pattern held, and got sharper.

52% of men getting less than one match a day

52% of men getting less than one match a day

52% of men getting less than one match a day

a gender split as skewed as 74/26

a gender split as skewed as 74/26

a gender split as skewed as 74/26

inflated "likes" counts; and free features like city search progressively moved behind paywalls.

inflated "likes" counts; and free features like city search progressively moved behind paywalls.

inflated "likes" counts; and free features like city search progressively moved behind paywalls.

One user online tied the decline directly to Match Group's 2011 acquisition, describing a slow redirection toward the company's other apps.

One user online tied the decline directly to Match Group's 2011 acquisition, describing a slow redirection toward the company's other apps.

One user online tied the decline directly to Match Group's 2011 acquisition, describing a slow redirection toward the company's other apps.

distance and gender preferences routinely ignored, with profiles shown from thousands of kilometers away

distance and gender preferences routinely ignored, with profiles shown from thousands of kilometers away

distance and gender preferences routinely ignored, with profiles shown from thousands of kilometers away

Interviews

Interviews

We built questions from what we'd already found and stayed flexible on format.

We built questions from what we'd already found and stayed flexible on format.

In-person group sessions

In-person group sessions

Calls

Calls

Instagram DM

Instagram DM

We had participants build a profile live. That's how we independently reconfirmed our own photo-edit bug: we asked to change the picture mid-way, one participant hit the exact same wall we did, and had to be told the same workaround — delete first, then re-upload.

We had participants build a profile live. That's how we independently reconfirmed our own photo-edit bug: we asked to change the picture mid-way, one participant hit the exact same wall we did, and had to be told the same workaround — delete first, then re-upload.

What we heard?

What we heard?

The clearest pattern was the match score.

Participants converged on nearly identical language - "0%" trust, "not at all," "it seemed to say I match with everyone."

Mismatch in preferences

One participant who deleted the app within two hours said her preferences didn't hold and profiles kept showing up outside her age range, which she called "a bit creepy."

Skewed gender ratio

A small business owner in Jodhpur added: in a smaller city, an already skewed gender ratio made the app feel more unusable, and matched with scam profiles more than once.

One participant, describing how the same 15+ questions got asked regardless of intent, said the app needed separate question paths for long-term versus short-term users, since someone looking for casual "don't need much question." It wasn't the only input pointing that way, but it confirmed something we were already circling: intent needed to shape the questions themselves, not just filter results after the fact.

One participant, describing how the same 15+ questions got asked regardless of intent, said the app needed separate question paths for long-term versus short-term users, since someone looking for casual "don't need much question." It wasn't the only input pointing that way, but it confirmed something we were already circling: intent needed to shape the questions themselves, not just filter results after the fact.

A few also pointed to Hinge's on-the-fly location changes and inappropriate-language filter as things OkCupid lacked. Asked if they'd recommend the app to a friend, one answer summed up: "rehne de bhai" — roughly, "don't bother."

A few also pointed to Hinge's on-the-fly location changes and inappropriate-language filter as things OkCupid lacked. Asked if they'd recommend the app to a friend, one answer summed up: "rehne de bhai" — roughly, "don't bother."

Ideation

Ideation

With the interviews done, we clustered every note by shared theme and named each cluster: Low Profile Quality & Trust, Monetization & Paywalls, Onboarding & Question Relevance, Poor App Performance & UI, Ineffective Matching Algorithm.

With the interviews done, we clustered every note by shared theme and named each cluster: Low Profile Quality & Trust, Monetization & Paywalls, Onboarding & Question Relevance, Poor App Performance & UI, Ineffective Matching Algorithm.

Low Profile Quality & Trust stood above the rest — it was the only theme where every participant who raised it described a negative experience. That gave us a problem statement to design against:

Low Profile Quality & Trust stood above the rest — it was the only theme where every participant who raised it described a negative experience. That gave us a problem statement to design against:

How might we?

Users do not trust the app's profile Match Score. This creates a fundamental trust gap, as they do not believe the score is a genuine or accurate reflection of compatibility. How might we help users trust that who they see — and how they're matched; is real?

To design toward that

To design toward that

We first mapped the existing in-app journey for one task: "Like a profile that matches your trust score and feels meaningful." Actions, thoughts, pain points, emotions, and opportunities, step by step, pulled from the interviews and live testing.

We first mapped the existing in-app journey for one task: "Like a profile that matches your trust score and feels meaningful." Actions, thoughts, pain points, emotions, and opportunities, step by step, pulled from the interviews and live testing.

We first mapped the existing in-app journey for one task: “Like a profile that matches your trust score and feels meaningful.” Actions, thoughts, pain points, emotions, and opportunities, step by step, pulled from the interviews and live testing.

Then we mapped something the app itself never seems to have referenced: how dating actually works with no app involved. We asked participants openly how they decide to date someone, what makes them feel compatible, and where the pull toward wanting a partner even comes from.

Then we mapped something the app itself never seems to have referenced: how dating actually works with no app involved. We asked participants openly how they decide to date someone, what makes them feel compatible, and where the pull toward wanting a partner even comes from.

The answers sketched a consistent real-world arc

The answers sketched a consistent real-world arc

First Contact

First Contact

Light Interaction

Light Interaction

Building Familarity

Building Familarity

Exchanging Contact

Exchanging Contact

Informal Hangout

Informal Hangout

Dating

Dating

Commitment

Commitment

The gap was obvious: the app was trying to deliver the end of that arc — a confident match without any of the trust-building steps that make the end believable in real life. That comparison, more than anything from the competitor benchmarking, is what shaped the redesign.

The gap was obvious: the app was trying to deliver the end of that arc — a confident match without any of the trust-building steps that make the end believable in real life. That comparison, more than anything from the competitor benchmarking, is what shaped the redesign.

From there we ran a card sort with real users to restructure the information architecture, then re-mapped the app journey against it.

From there we ran a card sort with real users to restructure the information architecture, then re-mapped the app journey against it.

IA: existing vs new

IA: existing vs new

We started out the explorations with pen and paper wireframes, a lot of scribbling and tearing up, tested as low-fi prototypes until a flow held together against both the research and plain intuition.

We started out the explorations with pen and paper wireframes, a lot of scribbling and tearing up, tested as low-fi prototypes until a flow held together against both the research and plain intuition.

Solution

Solution

The redesign restructures onboarding around intent instead of a single generic form, and closes the loop on trust by showing users exactly why they were matched with someone — not just a number, but the reasoning behind it.

The redesign restructures onboarding around intent instead of a single generic form, and closes the loop on trust by showing users exactly why they were matched with someone — not just a number, but the reasoning behind it.

Design Decisions

Design Decisions

The redesign restructures onboarding around intent instead of a single generic form, and closes the loop on trust by showing users exactly why they were matched with someone — not just a number, but the reasoning behind it.

The redesign restructures onboarding around intent instead of a single generic form, and closes the loop on trust by showing users exactly why they were matched with someone — not just a number, but the reasoning behind it.

1.

1.

Onboarding may take longer, and that's fine - it just has to pay off.

Onboarding may take longer, and that's fine - it just has to pay off.

A profile-building app asking for real information upfront isn't the problem; the original flow's failure wasn't its length, it was that all that invested effort led nowhere trustworthy. So instead of racing to shorten it, we staged it:

Credentials

Credentials

Personal Details

Personal Details

Personality Questions

Personality Questions

with a visible progress bar and skippable questions throughout, so time spent is at least spent knowingly. Mobile-number authentication replaces email as the primary path, cutting down fake accounts at the source.

2.

2.

Every intent gets its own question path.

Every intent gets its own question path.

Long-term, short-term, casual, and hookup each run a distinct set of questions instead of sharing one generic list — so a short-term user is never mid-flow answering about wanting kids )

3.

3.

A confidence cue replaces blind faith

A confidence cue replaces blind faith

Added a nudge "x people around your preferred location have same goal" on the intent screen. It gives users a concrete reason to believe a match is possible before they've invested any time.

Banners, and small cues are placed all over the flow, so the user feels confident and assured moving ahead

4.

4.

The bio gets built from answers users already gave

The bio gets built from answers users already gave

AI-generated bio prompts pull from the intent and personality answers already collected, so writing a bio isn't a blank-page problem.

We considered the reverse order too — asking for the bio first, then using it to shape suggestions, but sequencing it after the rest of the flow gives the system more signal to work with.

5.

5.

A preview before you're visible to anyone.

A preview before you're visible to anyone.

Users see exactly how their profile will look to others before it goes live, with the option to edit directly from that preview instead of backtracking through settings.

6.

6.

Must-haves are visible upfront, not discovered mid-conversation

Must-haves are visible upfront, not discovered mid-conversation

Users define their non-negotiables right after their intent questions, and those appear as scannable tags on every profile card.

7.

7.

"Why this match?" on every profile.

"Why this match?" on every profile.

The score is no longer a number taken on faith. Every match shows the shared must-haves, hobbies, and question answers that produced the score — the trust score finally shows its work.

Outcome

Outcome

To close the loop, we ran an informal usability round on the redesigned flow with 12 people - a mix of classmates, professors, and some of our original interview participants, and asked each for an honest star rating plus a reason.

To close the loop, we ran an informal usability round on the redesigned flow with 12 people - a mix of classmates, professors, and some of our original interview participants, and asked each for an honest star rating plus a reason.

5

5

rated from 10 participants out of 12

Very Positive

Very Positive

The dominant reaction when testing the redesigned flow

The 83% who gave a full 5 stars independently landed on the same reason: the experience no longer felt pushed. Nothing forced, nothing interrupting — a direct reversal of the paywall-heavy pattern that drove the original complaints.

The 83% who gave a full 5 stars independently landed on the same reason: the experience no longer felt pushed. Nothing forced, nothing interrupting — a direct reversal of the paywall-heavy pattern that drove the original complaints.

The remaining 17% rated it 4.5 stars, flagging that match suggestions still surfaced people outside their stated age range. Worth naming honestly: that's a matching-algorithm issue, not a design one — a backend behavior outside what an interface and flow redesign can fix on its own, and a real limitation of doing this as a project without access to the actual system underneath.

The remaining 17% rated it 4.5 stars, flagging that match suggestions still surfaced people outside their stated age range. Worth naming honestly: that's a matching-algorithm issue, not a design one — a backend behavior outside what an interface and flow redesign can fix on its own, and a real limitation of doing this as a project without access to the actual system underneath.

Reflection

Reflection

This started as a five-minute curiosity — download the app, see what "match %" actually meant — and turned into the longest research process either of us had run. Looking back, three things changed how we'd approach the next one.

This started as a five-minute curiosity — download the app, see what "match %" actually meant — and turned into the longest research process either of us had run. Looking back, three things changed how we'd approach the next one.

This started as a five-minute curiosity — download the app, see what “match %” actually meant — and turned into the longest research process either of us had run. Looking back, three things changed how we’d approach the next one.

1.

The strongest finding wasn't planned — it emerged from paying attention.

We didn't set out to test the trust score specifically. It became the story because it kept showing up, unprompted, the longer we used the app honestly.

2.

Trust is an architecture decision, not a feature.

A badge or a number doesn't fix it. Every score needs a "why" a user can actually open and check — that's what the redesign is really built around.

3.

The best spec wasn't a competitor app — it was real behavior.

Comparing OkCupid to Hinge or Bumble told us what features existed elsewhere. Mapping how people actually build relationships offline told us what was missing here — and that's what drove the biggest structural change.

This started as a five-minute curiosity — download the app, see what "match %" actually meant — and turned into the longest research process either of us had run. Looking back, three things changed how we'd approach the next one.

This started as a five-minute curiosity — download the app, see what "match %" actually meant — and turned into the longest research process either of us had run. Looking back, three things changed how we'd approach the next one.

fin

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